Autoencoder-based structures have dominated recent learned image compression methods. However, the inherent information loss associated with autoencoders limits their rate-distortion performance at high bit rates and restricts their flexibility of rate adaptation. In this paper, we present a variable-rate image compression model based on invertible transform to overcome these limitations. Specifically, we design a lightweightmulti-scale invertible neural network, which bijectively maps the input image into multi-scale latent representations. To improve the compression efficiency, a multi-scale spatial-channel context model with extended gain units is devised to estimate the entropy of the latent representation from high to low levels. Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared to existing variable-rate methods, and remains competitive with recent multi-model approaches. Notably, our method is the first learned image compression solution that outperforms VVC across a very wide range of bit rates using a single model, especially at high bit rates.

With the rapid expansion of image data and advancements in artificial intelligence, a significant portion of image analysis is performed by machines rather than humans. To enhance efficiency in data transmission and visual analysis, on-demand transmission becomes a preferable approach, which adaptively transmits the necessary information based on specific requirements. In this paper, we propose a novel joint feature and image compression scheme to facilitate flexible on-demand transmission. The bitstreams generated by the proposed scheme can be adapted to multiple machine vision tasks and image reconstruction based on specific needs. To achieve a good balance between the feature-based visual analysis performance and computational overhead at the receiver side, we adopt a reversible neural network as the feature extractor. The extracted features contain all information from the original image and necessitate a low-complexity analysis network. Additionally, we develop end-to-end compression models for multi-granularity features and image signals, where prediction models are incorporated in both feature/image space and latent space to improve the efficiency of joint compression. Furthermore, several feature transform blocks are designed to align the features with the requirements of different tasks. Experimental results on the COCO dataset show that the proposed compression method outperforms state-of-the-art image codecs on several machine vision tasks, and can also achieve comparable results in terms of image reconstruction.

Reconstruction-free image compression for machine vision aims to perform machine vision tasks directly on compressed-domain representations instead of reconstructed images. Existing reports have validated the feasibility of compressed-domain machine vision. However, we observe that when using recently learned compression models, the performance gap between compressed-domain and pixel-domain vision tasks is still large due to the lack of some natural inductive biases in pixel-domain convolutional neural networks. In this article, we attempt to address this problem by transferring knowledge from the pixel domain to the compressed domain. A knowledge transfer loss defined at both output level and feature level is proposed to narrow the gap between the compressed domain and the pixel domain. In addition, we modify neural networks for pixel-domain vision tasks to better suit compressed-domain inputs. Experimental results on several machine vision tasks show that the proposed method improves the accuracy of compressed-domain vision tasks significantly, which even outperforms learning on reconstructed images while avoiding the computational cost of image reconstruction.

In an intelligent society, image compression needs to serve both human vision and machine vision. Traditional image compression schemes only consider visual quality for humans. In addition, the bitstream needs to be fully decoded to images before performing semantic analysis (e.g., by deep neural networks). These two factors make traditional image compression schemes semantically inefficient. To better serve the needs of both human vision and machine vision, it is more reasonable to compress and transmit image signals and features simultaneously. In this paper, we propose a novel end-to-end semantic scalable image compression method, which progressively compresses coarse-grained semantic features, fine-grained semantic features, and image signals. To utilize the cross-layer correlation between features and image signals, we propose a cross-layer context model to reduce the information redundancy, which takes higher-layer features as cross-layer priors to predict the probability distribution parameters for the entropy model of lower-layer features or images. Furthermore, we adopt a Region of Interest (ROI) compression scheme. The objects with rich semantic information and the background are compressed separately, to further improve the compression efficiency. Experimental results on the CUB-200-2011 and FGVC-Aircraft datasets demonstrate the effectiveness of our proposed scheme compared to separate compression of image signals and features.